Sat.Sep 21, 2024

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7 reasons why I choose Android over iOS (and sideloading is not one of them)

Dion Hinchcliffe's Web 2.0 Blog

Many years ago, I decided Android was the mobile operating system for me. But what is it about Google's OS that I find more appealing than Apple's?

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Time for a Government Turnaround: Efficiency, Innovation, and Accountability for the American Taxpayer

CEO Insider

Our nation was founded on the principle of “no taxation without representation,” yet today, you and I continue to fund a government that increasingly dictates what we can’t do while burdening us with inefficiencies and unnecessary costs. This situation must change. The time has come for a comprehensive turnaround in how the government operates.

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How Apple, Google, and Microsoft can save us from AI deepfakes

Dion Hinchcliffe's Web 2.0 Blog

To combat AI-driven disinformation, tech giants must collaborate to adopt these open standards. The absence of Apple and X raises concerns.

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Toppling Hierarchies Through Peer Networking for a Better Future of Work

CEO Insider

Traditional hierarchical structures are becoming obsolete in today’s fast-paced work environment, with their limitations in fostering innovation, quick decision-making, and employee engagement. Embracing peer networking as a modern management approach empowers employees, bridges the gap between leadership and operations, and unlocks the collective potential of teams.

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Launching LLM-Based Products: From Concept to Cash in 90 Days

Speaker: Christophe Louvion, Chief Product & Technology Officer of NRC Health and Tony Karrer, CTO at Aggregage

Christophe Louvion, Chief Product & Technology Officer of NRC Health, is here to take us through how he guided his company's recent experience of getting from concept to launch and sales of products within 90 days. In this exclusive webinar, Christophe will cover key aspects of his journey, including: LLM Development & Quick Wins 🤖 Understand how LLMs differ from traditional software, identifying opportunities for rapid development and deployment.

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I bought the iPhone 16 Pro for just one reason - and AI's got nothing to do with it

Dion Hinchcliffe's Web 2.0 Blog

If you're also an avid photographer but don't want to carry your digital camera everywhere, upgrading this year might be wise.

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Upgrade to Windows 11 Pro for $20 right now - here's how

Dion Hinchcliffe's Web 2.0 Blog

Save 89% on a Windows 11 Pro license for more productivity features to help you get things done.

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Student Entrepreneur’s Guide to Business Expenses, Tax Deductibles and Cost Management

Women on Business

We've Moved! Update your Reader Now. This feed has moved to: [link] If you haven't already done so, update your reader now with this changed subscription address to get your latest updates from us.

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Learn a new language with 74% off a Babbel subscription right now

Dion Hinchcliffe's Web 2.0 Blog

Save $449 on a Babbel Language Learning subscription and learn 14 new languages with this deal.

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Differential Privacy: A Primer

Abhishek Tiwari

Differential Privacy (DP) is a mathematical framework that protects individual privacy in data analysis while allowing useful insights to be extracted. It works by adding carefully calibrated noise to data or query results, ensuring that including or excluding any single individual's data doesn't significantly change the analysis outcomes.

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An Architect’s Guide for Selecting Scalable, Data-Layer Technologies

There’s no getting around it: selecting the right foundational data-layer components is crucial for long-term application success. That’s why we developed this white paper to give you insights into four key open-source technologies – Apache Cassandra®, Apache Kafka®, Apache Spark™, and OpenSearch® – and how to leverage them for lasting success. Discover everything you’ll want to know about scalable, data-layer technologies: Learn when to choose these technologies and when to avoid them Explore h

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Managing Differential Privacy in Large Scale Systems

Abhishek Tiwari

The promise of differential privacy is compelling. It offers a rigorous, provable guarantee of individual privacy, even in the face of arbitrary background knowledge. Rather than relying on anonymization techniques that can often be defeated, differential privacy works by injecting carefully calibrated noise into computations. This allows aggregate statistics and insights to be extracted from data while masking the contributions of any single individual.

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